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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Sharded mutable dense hash table."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import range
from tensorflow.contrib import lookup
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import math_ops
class ShardedMutableDenseHashTable(lookup.LookupInterface):
"""A sharded version of MutableDenseHashTable.
It is designed to be interface compatible with LookupInterface and
MutableDenseHashTable, with the exception of the export method, which is
replaced by an export_sharded method.
The _ShardedMutableDenseHashTable keeps `num_shards` MutableDenseHashTable
internally. The shard is computed via the modulo operation on the key.
"""
# TODO(andreasst): consider moving this to lookup module
def __init__(self,
key_dtype,
value_dtype,
default_value,
empty_key,
deleted_key,
num_shards=1,
checkpoint=True,
name='ShardedMutableHashTable'):
with ops.name_scope(name, 'sharded_mutable_hash_table') as scope:
super(ShardedMutableDenseHashTable, self).__init__(key_dtype,
value_dtype, scope)
table_shards = []
for i in range(num_shards):
table_shards.append(
lookup.MutableDenseHashTable(
key_dtype=key_dtype,
value_dtype=value_dtype,
default_value=default_value,
empty_key=empty_key,
deleted_key=deleted_key,
checkpoint=checkpoint,
name='%s-%d-of-%d' % (name, i + 1, num_shards)))
self._table_shards = table_shards
# TODO(andreasst): add a value_shape() method to LookupInterface
# pylint: disable=protected-access
self._value_shape = self._table_shards[0]._value_shape
# pylint: enable=protected-access
@property
def _num_shards(self):
return len(self._table_shards)
@property
def table_shards(self):
return self._table_shards
def size(self, name=None):
with ops.name_scope(name, 'sharded_mutable_hash_table_size'):
sizes = [
self._table_shards[i].size() for i in range(self._num_shards)
]
return math_ops.add_n(sizes)
def _shard_indices(self, keys):
key_shape = keys.get_shape()
if key_shape.ndims > 1:
# If keys are a matrix (i.e. a single key is a vector), we use the first
# element of each key vector to determine the shard.
keys = array_ops.slice(keys, [0, 0], [key_shape[0].value, 1])
keys = array_ops.reshape(keys, [-1])
indices = math_ops.mod(math_ops.abs(keys), self._num_shards)
return math_ops.cast(indices, dtypes.int32)
def _check_keys(self, keys):
if not keys.get_shape().is_fully_defined():
raise ValueError('Key shape must be fully defined, got %s.' %
keys.get_shape())
if keys.get_shape().ndims != 1 and keys.get_shape().ndims != 2:
raise ValueError('Expected a vector or matrix for keys, got %s.' %
keys.get_shape())
def lookup(self, keys, name=None):
if keys.dtype.base_dtype != self._key_dtype:
raise TypeError('Signature mismatch. Keys must be dtype %s, got %s.' %
(self._key_dtype, keys.dtype))
self._check_keys(keys)
num_shards = self._num_shards
if num_shards == 1:
return self._table_shards[0].lookup(keys, name=name)
shard_indices = self._shard_indices(keys)
# TODO(andreasst): support 'keys' that are not vectors
key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
num_shards)
value_shards = [
self._table_shards[i].lookup(key_shards[i], name=name)
for i in range(num_shards)
]
num_keys = keys.get_shape().dims[0]
original_indices = math_ops.range(num_keys)
partitioned_indices = data_flow_ops.dynamic_partition(original_indices,
shard_indices,
num_shards)
result = data_flow_ops.dynamic_stitch(partitioned_indices, value_shards)
result.set_shape(
tensor_shape.TensorShape([num_keys]).concatenate(self._value_shape))
return result
def insert(self, keys, values, name=None):
self._check_keys(keys)
num_shards = self._num_shards
if num_shards == 1:
return self._table_shards[0].insert(keys, values, name=name)
shard_indices = self._shard_indices(keys)
# TODO(andreasst): support 'keys' that are not vectors
key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
num_shards)
value_shards = data_flow_ops.dynamic_partition(values, shard_indices,
num_shards)
return_values = [
self._table_shards[i].insert(key_shards[i], value_shards[i], name=name)
for i in range(num_shards)
]
return control_flow_ops.group(*return_values)
def export_sharded(self, name=None):
"""Returns lists of the keys and values tensors in the sharded table.
Args:
name: name of the table.
Returns:
A pair of lists with the first list containing the key tensors and the
second list containing the value tensors from each shard.
"""
keys_list = []
values_list = []
for table_shard in self._table_shards:
exported_keys, exported_values = table_shard.export(name=name)
keys_list.append(exported_keys)
values_list.append(exported_values)
return keys_list, values_list
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